In Situ Anomaly Detection in Turbulent Reacting Flows at the Exascale

Anomaly detection is an unsupervised machine learning approach to detect outliers in data. Frequently, principal components analysis is used to flag outliers in data with large deviations from the principal components. This method may miss some data with anomalous behavior, and hence, here we use a methodology that is centered on analyzing fourth-order joint moments (co-kurtosis), particularly focusing on its application in multivariate combustion problems with large numbers of species. An in situ co-kurtosis algorithm is employed as the anomaly detection method, facilitated by a flyweight in situ visualization and analysis infrastructure for multi-physics HPC simulations (Ascent). We apply this algorithm on-the-fly to exascale high-fidelity simulations of reacting flows, performed using an adaptive mesh refinement solver (PeleC). We demonstrate the ability of the method to detect and identify the onset of low and high temperature ignition which is used for computational steering, as chemical and combustion anomalies occur intermittently at spatio-temporal locations unknown a priori. Furthermore, through a scalability analysis, we show that the relative computational cost of this in-situ anomaly detection algorithm compared to an iteration of the reacting flow solver is small.